{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3012d15",
   "metadata": {},
   "source": [
    "## Step 1.差分表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e03d5e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calNDDTabel(xilist,yilist):\n",
    "    xilist=np.array(xilist)\n",
    "    yilist=np.array(yilist)\n",
    "    #运行快，高级对象，有许多功能可以直接用\n",
    "    n=xilist.size\n",
    "    dimension=(n,n)\n",
    "    arrNDD=np.zeros(dimension)\n",
    "    arrNDD[:,0]=yilist\n",
    "    for j in range(1,n):\n",
    "        for i in range(j,n):\n",
    "            arrNDD[i,j]=(arrNDD[i,j-1]-arrNDD[i-1,j-1])/(xilist[i]-xilist[i-j])\n",
    "    return arrNDD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f582866",
   "metadata": {},
   "outputs": [],
   "source": [
    "xilist=[1,2,3,4,5]\n",
    "yilist=[0,0,2,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "adbe6fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31acc1ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.        ,  0.        ,  0.        ,  0.        ],\n",
       "       [ 3.        ,  1.        ,  0.        ,  0.        ],\n",
       "       [ 2.        , -1.        , -1.        ,  0.        ],\n",
       "       [ 1.        , -1.        ,  0.        ,  0.33333333]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calNDDTabel(xilist,yilist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25b84c51",
   "metadata": {},
   "source": [
    "## step 2.NewtonDD interpolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27255378",
   "metadata": {},
   "outputs": [],
   "source": [
    "def interpNDD(x,xilist,yilist):\n",
    "    xilist=np.array(xilist)\n",
    "    yilist=np.array(yilist)\n",
    "    n=xilist.size\n",
    "    NDDtable=calNDDTabel(xilist,yilist)\n",
    "    diagNDD=np.diag(NDDtable)\n",
    "    y=0\n",
    "    for i in range(0,n):\n",
    "        li=diagNDD[i]\n",
    "        for j in range (0,i):\n",
    "            li=li*(x-xilist[j])\n",
    "        y=y+li\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75f504bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.247"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpNDD(1.1,xilist,yilist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95f9f35c",
   "metadata": {},
   "source": [
    "## Step 3.Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e556873d",
   "metadata": {},
   "outputs": [],
   "source": [
    "xlist=np.arange(0.6,4.4,0.1)\n",
    "ylist=[interpNDD(x,xilist,yilist) for x in xlist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "67de819f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2cfe5a33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e1606842e0>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xilist, yilist, c = 'yellow', edgecolors = 'blue', s = 1200,marker = '*')\n",
    "plt.xlabel(\"X\", fontsize = 20)\n",
    "plt.ylabel(\"Y\", fontsize = 20)\n",
    "plt.plot(xlist, ylist, 'b-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "39312c2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xilist=[1,2,3,4,5]\n",
    "yilist=[0,0,2,0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "77469e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "xlist=np.arange(0.6,5.4,0.1)\n",
    "ylist=[interpNDD(x,xilist,yilist) for x in xlist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "78318397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e1606e7550>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xilist, yilist, c = 'yellow', edgecolors = 'blue', s = 1200,marker = '*')\n",
    "plt.xlabel(\"X\", fontsize = 20)\n",
    "plt.ylabel(\"Y\", fontsize = 20)\n",
    "plt.plot(xlist, ylist, 'b-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "89159e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "xilist=[1,2.5,3,3.5,5]\n",
    "yilist=[0,0.6,2,0.6,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "598719fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e160b4ec70>]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ua++ZINLS8qhWLZe0NFv8xhzvu+/c+4YlEwPYILwpXlraQUaNepq0tINeh2J8xs+D72DJJOy6dXNfravLFLZlC2zYUI1HHvkz334bY/O4meOkp0NMjJtNw48smYRZ06bQpo0lE3Oi999Xhgz5mDp1shgy5GPef9+uCzbHpKdDUpKbSsWPLJl4wAbhTTBpaftJTf03AKmp/yYtbb/HERk/8fPgO1gy8URyMqxb59aFNwbcrAgrVsRy/vkzADj//BksXx5rsyUYALZvd68RSybmOPkviOU2MY0JmDQJLrroU2rWzAagVq0jDB48nQ8+8DYu4w9+H3wHnycTEUkQkVkislpEvg7MFVZ4m74isk9ElgVuD3sRa1kUXNvEGMjv4hp/3H2pqeOtq8sAx94r8mfR8COfTWJ8gqPA71U1XUTqAUtEZIaqflNou3mqWtRiXb7TogU0b27JxDg//QRffRXLoEHTjrt/0KBpXH99LD/9BE2aeBSc8YX0dHcxa716XkdSNF+3TFR1u6qmB74/AKwGWnkbVeWwQXiTb/JkOP/82dSpk3Xc/XFxhxg4cA5TpngUmPENvw++g/9bJj8TkXZAN2BRkId7iMhyYBtwj6p+Hc7YyiM5GaZNg0OHoE4dr6MxlWXXLphexmlRX301k9tvfyvoY6mpbzF27HnUrBlXpn1ecIErQzeRb9cud/X7bbd5HUnxIiKZiEhdIA24S1ULdyKnA21V9aCIDAY+ADoUsZ8RwAiANm3ahC7gUkhOdgvdrFwJ55zjaSimEn37Ldx441FiYzMZNGgeIiVfK9K58x6GDPkw6GNDh05h7txBfPRRoxL3oypMm9aL7Ow4Zs6sbskkSuTPlmEtkwoKLBmcBrytqu8XfrxgclHVT0RknIg0VdVdQbZ9GXgZICUlxdMrws46y31NT7dkEk169ID09OpccUUM2dkxvPLKVTRuvKfc+6tbN5N//evaErfbvbsRN974Nu3axfDOO9V9tz64Kb9IqOQCn4+ZiIgArwGrVfXpIrZpHtgOEemOO6afwhdl+SQkuG6IRcE67UxE69gRFi6sS0JCf7p1W8u8eeeF9PnmzTuPbt3W0qZNPxYurGuJJMp8+SUkJkKjkhunnvJ1MgF6AtcA/QuU/g4WkZtF5ObANpcDqwJjJs8BV0TC+vQi0KsXzJ3rdSSmsMpYT6RmTfjnP2syblw8v/71pzz66KPk5lbu6Xb0aAyPPPIXfv3rTxk3Lp5nnqlFzZoV36+tp+IfeXnuPaJ3b68jKZmvu7lUdT4gJWwzFhgbnogqV9++7mK1775z83UZ7+WvJ7JhA7RvX/H9XXQRpKfX4ZprRvHZZxcyYcKlJCQcm8Hx0KHafP99q+NuIkqrVt/TqtX3tG69lZYtt1G79uHj9rtlS2uuuuoDYmM7kZ5ehxYtKh4rVP7xm4r55hs3AN+3r9eRlMzXySTa9enjvs6ZA9dc420sxnHriShpacIf/lA5LYkWLWD69Dj+8Y8udOmymgEDPiEj42QyMtqzd2/p+i4aN/6Jk0/ewEUXfUy9egd4/PHR/P73dfjDH6pTrRIbPKE4flN+c+a4r/nvFX4mEdAjFBIpKSm6ePFiT2PIy3PjJpdeCq+95mkoJuCcc/bTu/dLzJt3EwsX1q/w/o4ehfnzXQv0gw9cKxSUFi1+YMiQybRrt/nnVkj+TVV+bqVs3dr65++XLu3GwoW/AIRWreA3v3GvnR493NTklaGyj99UzLBhblx182bXNe41EVmiqilBH1TVKnk766yz1A8uuUT15JO9jsKoqn73nWqTJgc0M7O2Nm58ULdsKf++9uxRfegh1aZNVUG1Zk3VIUNUX39ddcMG1cGDs3TkyFe0LC/bW299VQcMyNJnn1W98ELV2Fi372bNVP/yF9X9+/1z/Kbi8vJUTzpJ9ZprvI7kGGCxFvECtXasx/r0cf3TthCS9ypjPZEDB+Bvf3PVN3/9qyuyePdd1+89ZQpcd50bi2jSJJdOnco202enTstp1SqPO+6ATz6BnTth4kQ4+2z405/cc44Z4y6ELQ9bT8Vf1qyBHTsio4sLsJaJ15YscZ8uJ0zwOhLTq9de/fDDi1QVnTLlYu3de2+pf/fQIdUxY461RIYOVV22LPi2R46oNmqUpVu3ttSyvGy3bm2pjRsf0iNHTtznl1+qDhrknrt5c9Xnn1c9fLgMB68VO35T+caNc//Pb7/1OpJjKKZl4vmbulc3vySTo0dVGzRQvfHG4+9ft86TcKqs7dtVGzQ4pIcPx6oqmpVVUxs0OKTbtxf/e3l5qq++qtqihTubLrhAddGi4n9n6lTVHj1Wanleur/4xSqdNq3ofc+dq9q7t4slIUH17bddjKE6flO5Cp73v/mNaqtWpfv/hUtxycS6uTwWE+O6QvKrNuBYeWZGhndxVTXlWU9kxw4YOhRuuMF1Xc2ZA59+Ct27F/9caWmHSE19M+hjR4/G8Nln/Tl6NPiIemrqm6SlZQV9DNxrafZsmDHDzUx91VVwxRWwe3fxMdl6Kt4reN6rutdTnz7+GHgvDUsmPtCnj1t5cft297Mrz8wlLS3P28CqkLKuJ/LRR3Dmme5N+9lnS39h2dGjMHmykJr63gmPbd7chr59v+Kqqz6mb9+v2Lz5xIuPUlPf44MP3H6KIgIDB8KCBfD44y5RdO4MM2cW/Tu2nor3Cp7369a5lRUjZrwErJvLD776ynVLTJzofu7efZ/ec88Tes45+7wNrIrYtUu1fv0szcysrQVfJgcP1tH69bN0165j2x48qHrTTe7/1aWL6qpVZXuuzz5TPeustVr4Jfnuu6kaH79fn3giR3NyVP/xjxyNj9+v77132QnbJiev0//9r/TPuWSJaqdOLua771bNyir/8ZvQKXjev/SS+3+tXet1VMfDxkz8nUxyclTr1XNvUlaeGX6vvaaamjpVg71ULrtsmr7+utvuyy9VO3RQFVH9wx/KPsCtqnrrrVn697//8ef9Z2bW1htvfEtPPvngCWMtixaptm9/UEeMePO4N/q//e0hHTky68SdFyMzU/W229wZf8YZqsuXl/34TegUPu8vucQVUvhpvES1+GRiV8D7QPXqcN55ro80KalweeYw7rgjQjpNfSBU64ksXBjHa69Bw4bwxz/CaadBWlrR+wy2nkheHkyalMfs2e8CsGLFmVxxxYckJ8eTnl6H+oWuEezeHZYujeOWW4aRktKXd94ZQufOK0lN/S/9+9/Pc89xwtXvxR1/jx5Quza8/DKkpMBNN7n7bD0V7xUsy7744o+ZPHkYgwZJxIyXgE2n4ht9+8LUqTBx4gEeeujfAKSm/psxY37JHXc08Da4CFLZ64kMGTKFp566j5df7kLz5jvp2XMJGRk5QYsjSlpPZMECaNJkBx06fMsLL9zO6NGP8/TTtbjmmqKHLuvXhwkT6vDvfycwYMBCRo++n1tvfZ5GjXaxcGEc555b9uPv3z+W+fNTGDu2CfPmfcM55yy29VQ8lpa2n3vvdef9uefOZPz4X0fEfFzHKarJEu03P3VzqaouXOi6IGrXPmLlmRW0Zo1q164H9LLLPtGffmqk5X2ZHD4cq1dfPV5B9frrX9Xs7OpFbvvTT430sss+0a5dD+iaNcHjuvvuw3rbbS/o0KHT9ayzDpS5/HvtWtXk5AM6dOh0ve22F3TUqCAXnJTy+LOyauoVV/yfgupNN72oOTkx5f47lfb4TXCFy7LHjbtZQXXOHK8jOxE2ZuL/ZJKd7abc6NBhgxYM9corP9AXX/Q4uAh0+LDqnXce1jZtdujcuedpWV8iu3c31L59/6eg+te//lHz8oredu7c87RNmx16111ZRY6j5OWptmlzUGNjs/Weew4HvfCwNI4cUf397w9rjRo52rbtgSL71Etz/Lm5ovff/3cF1Qsv/Fj3769b5r9TaY/fFG3cONXhwydp/t/z6qvHa82aWTpunMeBBWHJJAKSiapq48Y5mpCwWQuG+t57l+nAgVbVVV4ffaTavHmmPvLIo3r0aDUtzcsjI6Oddur0jdaocUQnTBhe5HY5OTE6evRftHnzTP3oo+Lj2LVLtX377GIvOCyLqVPd/kqqtCrN8b/00o0aE5OjXbuml+mq/LIcvynagAH7NC3tUlVF8/LQhITN2qPH57487y2ZREAy2bVLtWbNbAXVHTuaan6oVp5Zcdu2qQ4YcFB79/5Kv/uutRb30liypJuedNIP2rDhbp09u3eR2333XWvt1WuxDhhwULdt8+SwSq00xz916i+1bt392rr1d7pq1WlBt4nU4/ezwmXZGzYkKqg+9dRdvjzvi0smdtGiT0yeDD16LAFg7txjV7/FxR1i4MA5TJniVWSRL389kQsv7EJKytdMmvSroNulp3djwIDPqFXrMF98cS59+gRfBvP99y8lJeVrLrqoC9Onx1XawlShUprjHzToU+bPP4/c3Bj69/8f33yTVOT+Iu34/WzyZDj//NnUqeNmNZgzx12leMEFMyLuvLdqrhAob3nqLbeM5csvz2TOnD6kpr7/82NWnllx1arB/ffXoF+/GgwfPoEZM/7DU0+N/HkFw6VLuzJw4Ezq19/PnDl9aNdu8wn7yMqqxahR45g+/ddMmRLHOeeE+yjKr6TjB+jSZQWzZvWjb9/Z9O//P2bN6kdS0pqfH4/k4w+HyihLnzOnD02b7uS0076JuPPeFscKgQULYODAspWn1q+/hzFjbuXSSyfx44/NWLGiy8+PHTwYxz33jGP//rKXZ/boUaFDiUr79sHw4YdJTJzA2LE3smxZFwYM+Iy4uEzmzOlDYuKmoL83cuSrbNp0FRMn1jrhmpBIUvj4C1uzpiN9+84GYPbsvnTqtBaInuMPlYqc93XrZgLQrt1GzjprCWlpl/vyvLfFsTwYMylveepf/vKgguquXY3LfFhWnll611xzUJ9//jZdtqyzNm68S1u3/k43bEjU4v6+zz13u/72t5keRl158o+/qGP95ptOetJJP2jz5tt0zZpTNdqOP1QqUpa+cWNbBdVnn729TL8XzvOeSB6ABwYBa4H1wP1BHhfgucDjK4Dk0uw3HAPw5SlPnTevp4Lq++//qix/JivPLIP89USmTx+gTZrs1Fattuj69e21pL9xceuJRJLSrqfy9ddJGh//o7Zo8b2uXdshao4/1Mpblv7mm79VUF2+/MxS/064z/uITSZADLABaA/EAsuB0wptMxiYGkgqvwAWlWbf4azmKkt56uHDsVqr1iG9885nSnMYVp5ZDlOnqnbpsk6bNt2hrVpt0W+/PVlL+7IsaT2RSFCW9VRWrTpN4+N/1JYtt+q6dadExfGHS1nL0q+77jVt3HiX5uZKidt6dd4Xl0z8Xs3VHVivqhmqmg28A1xSaJtLgPGBY10INBQRX9WXXHQRpKfXYe7cUfTvv4gtW1oXuW3Nmtn06LGA2bP7lrjfLVta07//IubNu5v09DpcdFElBh3F3nwzi4yMZsTGZjNrVj9OOWXDz49VdD2RSFCW9VROP/0b/ve//mRnxzJw4EwGDnw/4o8/XMpy3qu68aleveZRrVrxYy2+Pe9LTIHetkwuB14t8PM1wNhC23wEnFfg58+AlCL2NwJYDCxu06ZNxdN0GeXmqj72WLaedNK+YruxHnvsPgXVzZsTitwmLe1SPemkffr44zmamxv2Q4lY27apxsTkaoMGe/Sbbzppwb/ppk1ttGfPdG3WLEt79kzXTZvaaOG/e0ZGO42PP6Q5OR4eRAXk5KjGxx/SjRvbauFjK+7409O7ar16+7Rjx9XapEnkHr8XSnPer1p1moLqCy/cEvRxv5z3RHA317AgyeT5Qtt8HCSZnFXSvr28aHHhQje1+C23vKaHDtU6Ibx1605RUH3mmTtPeOzQoVp6882va/v2B3XhQs8OISIdPKjasaOqSK5+/nkPLfh3DeV6In5SkfVUZswYoDVqHNG6dTOtq6scijvvH3nkTyqSq9u2Nddgb1l+Oe8jOZn0AD4t8PMDwAOFtnkJuLLAz2uBFiXt2+sr4PfuVR08OEtHjnwlWHh65pnL9bzz5p5w/623vqqDB2fpPv/NtOBr2dmqgweriuTp1Vf/W/P/nuFaT8QvKrqeyttvX6mgevLJR61FXA5FnfedOy/Tnj3naVFvWX4574tLJn4fM/kK6CAiiSISC1wBFL4mdArwW3F+AexT1e3hDrSsGjSAJk1y6dRpedDHU1PT+PzznvzwQ7Pj7u/UaTlNm+ZZnX8ZqMKIEfDJJ1C/fjZ/+tOjgFtPJCVlNYcOXU56etwJa7fnrydy8OAwUlJWs2LFmQCkpv6XSZNyyYuwVZXz11NJTT22nkpZj3/48Ince+/jbNgQw113ub+tKb1g5/369SezYkUXUlOLXiAnIs77orKMX264aq11uKquBwP33QzcHPhegBcCj6+kiPGSwjevWyYllWeuXHm6guq4cTcfd7+VZ5bdgw+qgup116meccZGzctDx469XZs2zdTx40v+eJ2Xp/rWW7natGmmjh17u+bloaefvkk//zwMwVei+fMr7/ibNNmnoPqPf4Qh8CgS7Lx//PF7FTToGJ3fznuKaZnYFfAemTYNHn10FV98cWbQx1WhU6c1JCRsYebM8497rEePVYwefTq//GU4Io1s48bByJFw440QF3eEo0df47vvOvD99z2YOLEuHTqUfl/r1sGVVx6kdesFtGnzLbGxN/DUU7GhC76SjRp1hJycyjn+hIRvmTnzJtaujWH8eLjmmtDFHU2Cnffduy9CVfjqq+7F/KY/znu7At6HLZMbbsjUMWN+Hyw0zcmJ0Zkz++t99z2mMTE5J1wN/+ST9+iNNx7yLvgIMWWKW699yBA3ZhLO9UT8JhTrqbRpc0AHDFCtXt0N7JuSFT7vN29OUFB97LH7fj7vi1qozA/nPZE6AB/Km5fJpLTlmV26rFZQfe21647bJtLLU8Nh2TLVuDjVs85Szcz0bj0RvwjV8W/cqHr66aoNG7qVIE3Rgp33zzxzp4LqrFm9I6Is3ZKJz5JJacszH388R6tVy9Xk5BP/f5Fcnhpq27apJiSotm6t+v33XkcT/TIyVOPjVU85RSMmuXoh2Hl/3nlztU2bTRFTlm7JxGfJpCzlmcOHq0Ke/r//93bUlKeGUmam6tlnu1bJ0qVeR1N1fP65W3a6Tx/1fJDYrwqf9xs2tFPI00aNjkRMWbolEx8lk9xc1RYtMnXt2g6qii5ffqYmJW3Sq67KDFpD/vnn7r/Uo8dhTUra9PMkcGvWnKotWx60Wv8CcnNVL7/cjZN88IHX0VQ9b7/tXqvXX68RM5YULsHO++bNdymoLlgQ/Hf27VMdPjzTV+e9JRMfJZOylmfm5qq2bKl66aXRUZ4aSg895F7RTz7pdSRV18MPu//BE094HYm/BDvvTzstT089tfjE67ey9OKSiZUGh1l5yjNvuw1efx127oTvv4/s8tRQmTDBlaf+7nfwyisg4nVEVZMqXHkl/Pe/8P778KtfeR2RPxQ+78eNq8u558K998Lf/17y7/ulLN1Kg33SMilveeasWe7T3rvvup8jtTw1VObPV42NVe3Xz/rr/eDQIdXu3VXr1FFdssTraLwX7Lx/7TV3Tpfl7+OH854Ink4lquzeDdWrxzJlSg2efLImsaX8YNGrF8THQ1pgtoXYWBgzpiZTplQnJqYmu3eHLma/27wZLr0U2raF996j1H9TEzq1a8PkydCkCVxyCfzwg9cReSvYeZ+WBu3aQbdupd+P38976+aKECNGwMSJrqurVi2vo/GHgwehZ0+XUBYuhE6dvI7IFLR0KZx3HnTuDLNm2es237597sPhHXfAmDFeR1M2xXVzWcskQqSmujfPGTO8jsQf8vLgt7+FVavgP/+xROJH3brB+PEu0d90k00Kme+jjyAnx53T0cSSSYTo1w8aNjzW1VXV/fnPMGkSPPUUNkeZj6WmwqOPuqQSaZ/CQyUtDVq2hHPO8TqSymXJJELExsLQoTBlivtUU5W98w789a+ucuvOO72OxpTkoYfgN7+B++5zn8qrssxMN9njZZdBtSh7942yw4lul18Oe/a4dTmqqsWL4brrXFHCuHFWAhwJRFxpe3IyDB8OX3/tdUTemTQJsrKir4sLbADe6zDK5OhROPlkSEyE2bO9jib8tm2Ds8+GGjXgq6/cIKaJHFu3uv9fnTqwaBE0bep1ROGlCmedBYcPu7G+SGyZ2AB8lKhe3VWAzJkDS5Z4HU14ZWW5C+D27XNdfZZIIk/r1vDBB+7C28svh+xsryMKrzlzXIXb3XdHZiIpSRQeUnS74QaoVw+eecbrSMInLw+uvdZ1cb39tis1NZHpnHNcl9ecOXDLLVWrwuvpp92HoKuv9jqS0LBkEmEaNHAJ5T//cd0GVcGf/wzvvgtPPOEugjORbfhwePhhl1SqSoXXunXw4Ydw663uos5oZMkkAt1xh/u0/vzzXkcSehMmHKvc+v3vvY7GVJbRo49VeH3wgdfRhN4zz0DNmq41Fq18m0xE5EkRWSMiK0Rkkog0LGK7TSKyUkSWiUhkjaiXU7t2rhrkpZfchYzR6vPPXRLp29cqt6KNCLzxhhuQv+oqN5YQrXbtgrfect1bzZp5HU3o+DaZADOAM1S1M7AOeKCYbfupateiqgyi0ahRbjD6jTe8jiQ0MjLcgHvbtu4iL5tzK/oUnMNryBBXrReNXnrJFZDcfbfXkYSWb5OJqk5X1aOBHxcCrb2Mx29+8Qs491z45z8hN9fraCrXvn3uzSU3113k1rix1xGZUGne3P2P9+1zF+UeOuR1RJXryBEYOxYGDYLTT/c6mtDybTIp5HpgahGPKTBdRJaIyIjidiIiI0RksYgs3rlzZ6UHGW6jRrlP8JMnex1J5Tl61PWlr1vnWiSnnup1RCbUOnd2k5imp7v51vLyvI6o8kyc6GZNHjXK60hCz9OLFkVkJtA8yEMPqurkwDYPAinAZRokWBFpqarbROQkXNfY7ao6t6TnjsSLFgvLzYUOHdw8P/Pnex1Nxam6MZI33nALXN1wg9cRmXB65hn3pnvHHa7FHeljZKrQpYv7fvnyyD8eKP6ixerhDqYgVR1Y3OMici1wMTAgWCIJ7GNb4OsOEZkEdAdKTCbRICYG7rrLzU+1aFHkTxz34IMukTz8sCWSquiuu2DLFpdUWrSA++/3OqKK+ewzWLnSvaajIZGUxLfdXCIyCLgPGKqqQXtSRSROROrlfw9cAKwKX5Teu+46d+1JpF/E+Nxz8Nhjbt2W0aO9jsZ4QcRddzJ8ODzwQOQXlzz9tKveuvJKryMJD98mE2AsUA+YESj7/Re4bi0RyZ/qsBkwX0SWA18CH6vqNG/C9Ua9eu4N+L333CJRkeg//3GfSn/1KysBruqqVXNJ5Pzz4cYbI3eW4W++galT4bbb3PUlVYFN9BgFtmyB9u3h+utdGWIkmTkTBg921Wmffhq9VwebsjlwAPr3dzMMf/YZ9OjhdURlM3y4uxjzu++ia0JLm+gxyiUkwO23w8svR9Zswunpbv32Tp3c5I2WSEy+evXg44+hVSu46CL3ST9SfPihq+L6wx+iK5GUxFomUeLQIVc5kpsLK1ZA3bpeR1S89evd+u21asGCBa4izZjCNm5011NVrw5ffOE+OPnZnj3uepL4eLdMQrRdbGstkyqgTh03cd6mTW7w0s/WrnVTpOTmuq4tSySmKImJbuxh/37o08e9vv3srrtgxw54883oSyQlsWQSRXr1cjX6Y8e6Kb79aOVK6N3bLT08a5br4jKmOF27urG1vXvda/zbb72OKLiPPnJr3f/xj9Ctm9fRhJ91c0WZzEzX3aXqurvi4ryO6Jj0dFelU7u2G1Tt2NHriEwkWb7cvX5iYlxy8dP0JHv2wBlnuHnGFi+O3laJdXNVIXFxrrRy40Z/dXctWOCqc+rVg7lzLZGYsuvSxbW4RVw36bJlXkd0zKhR8OOPVbN7K58lkyjUq5er7nr+eX90d82e7T5Rxse7RNK+vdcRmUiVlOReQ3XqQL9+8OWXXkfkqs7efNN9eEtO9joa71g3V5TKzDy2vK2X3V2ffuouRmzf3nVNtGjhTRwmumze7Fq6O3e6sYrevb2JY+9e193WuLHr3or2CxStm6sKiotz1V0ZGXDvveFfazsvDx5/3F0j0LGja51YIjGVpW1b10Jp1QoGDnTT8YT7Na7q5sX78UfXtRztiaQklkyiWJ8+rlRx3DhX5RWudU927XLrkTzwAFx+uTvp4+PD89ym6mjVyq3GOWiQe1MfNsytixIOOTluhuv86q2UKrMsX9EsmUS5p55ya6ePHQuXXea6v0Lpiy9cWeTMmS6JTZwI9euH9jlN1dW4sVvPZ8wYN31JcrKrGgylffvcFEBvvAF//jM88khony9SWDKJctWquRPt+edd33K/fq5ZXtlU3fP06eOqWRYsgFtusUkbTeiJuA9Mc+dCdrabx+vFF0PT7bVliytwmT3bdSOPHm2v8XyWTKqI226DSZNg1So3qeLq1ZW37/Xr4ZJL3FxEQ4e6T4ZVuarFeOPcc2HpUhgwAG69Fa64onJn0l62zJ07mze7q/Kvu67y9h0NLJlUIUOHulLhQ4fciTe3gkuILVvmTtiOHWH6dHj2WTcVfoMGlRKuMWXWtKlrgT/2mPvwdMopcO21FZ8octo01yKpVs2tajqw2GX9qiZLJlXM2WfDwoXQvLk7IYYNg//+t2xjKfPmuT7jbt3gk0/gnnvcnEl33GFNfuO9atXcKo0bNrgW+XvvufLdSy91K5KW1oED8H//50rbL77YJaZFi+DMM0MWekSz60yqqD173PK4777rxlBq13YnzLBhLlHExcGRI7B1q7tt2eJuH3/sKmiaNnWVYiNHQsOGXh+NMUXbtcuNGT7/vHvd9+sHv/wltG7tZiFOSHCTjdas6RLIhx+682LqVHcOtGzpWuCjR7sZHKqy4q4zsWRSxeXmupbGu++6T3A7driri+Pi3AVhhSUmwt13u7LIOnXCH68x5XXggFvz59ln3Qejwpo1c5Vahw+7a6KGDYNf/9oN6FezPhzAkklQlkxOlJvrxlEmTXKfyBISjn16a93a3fw0caQx5XXw4LEWd8GvcXGQmurGFC2BnMiSSRCWTIwxpmxsOhVjjDEh5dtkIiKjReR7EVkWuA0uYrtBIrJWRNaLyP3hjtMYYwxU9zqAEjyjqmOKelBEYoAXgPOBrcBXIjJFVStYVW6MMaYsfNsyKaXuwHpVzVDVbOAd4BKPYzLGmCrH78nkNhFZISKvi0ijII+3AgoW+W0N3BeUiIwQkcUisnhnsLpXY4wx5eJpMhGRmSKyKsjtEuBF4GSgK7AdeCrYLoLcV2R5mqq+rKopqpoSb3OiG2NMpfF0zERVSzXDjYi8AnwU5KGtQEKBn1sD2yohNGOMMWXg224uESm4Lt+lwKogm30FdBCRRBGJBa4ApoQjPmOMMcf49qJFEfk3rotLgU3ATaq6XURaAq+q6uDAdoOBfwIxwOuq+rdS7n8nUIkTVFe6psAur4PwUFU+fjv2qsvvx99WVYOOEfg2mVR1IrK4qCtNq4KqfPx27FXz2CGyj9+33VzGGGMihyUTY4wxFWbJxL9e9joAj1Xl47djr7oi9vhtzMQYY0yFWcvEGGNMhVkyMcYYU2GWTHwmMA/ZDhEJdpFmVBORBBGZJSKrReRrEbnT65jCSURqiciXIrI8cPyPeB1TuIlIjIgsFZFgM15ELRHZJCIrA8ttROSqfTZm4jMi0hs4CIxX1TO8jiecArMetFDVdBGpBywBflVVlhQQEQHiVPWgiNQA5gN3qupCj0MLGxEZBaQA9VX1Yq/jCRcR2QSkqKqfL1gslrVMfEZV5wK7vY7DC6q6XVXTA98fAFZTzCzQ0Uadg4EfawRuVebTnoi0Bi4CXvU6FlN2lkyML4lIO6AbsMjjUMIq0M2zDNgBzFDVqnT8/wTuBfI8jsMLCkwXkSUiMsLrYMrDkonxHRGpC6QBd6nqfq/jCSdVzVXVrrgZsLuLSJXo6hSRi4EdqrrE61g80lNVk4ELgZGB7u6IYsnE+EpgrCANeFtV3/c6Hq+o6l5gNjDI20jCpicwNDB28A7QX0QmeBtS+KjqtsDXHcAk3CqyEcWSifGNwAD0a8BqVX3a63jCTUTiRaRh4PvawEBgjadBhYmqPqCqrVW1HW4pif+p6tUehxUWIhIXKDhBROKACwi+5IavWTLxGRGZCCwAOorIVhH5ndcxhVFP4Brcp9Jlgdtgr4MKoxbALBFZgVurZ4aqVqkS2SqqGTBfRJYDXwIfq+o0j2MqMysNNsYYU2HWMjHGGFNhlkyMMcZUmCUTY4wxFWbJxBhjTIVZMjHGGFNhlkyMMcZUmCUTYzwkIjeIiIrI1GK2+Tiwza3hjM2YsrBkYoyHVPVVYAowSERGFn5cRG4BBgNTVXVcuOMzprTsokVjPCYiJ+Gmz4gDklV1beD+U4GlQBZwhqr+4F2UxhTPWibGeCwwud+NQB1ggohUF5HqwITAfSMskRi/q+51AMYYUNXJIvI6cD3wcODus4E3q/LsySZyWDeXMT4RmDl2OdAmcNcWoHNg1UljfM26uYzxiUDSeBSICdxusURiIoUlE2N8IrCGyX0F7hrmVSzGlJUlE2P84wmgE/AssAy4XkSGeBqRMaVkYybG+ICIXABMw5UInw10ABYDe3Flwbu8i86YklnLxBiPiUhj4A0gB7haVY+o6irgT7hV+P7lZXzGlIYlE2O89yLQEnhIVVcUuP8pYB6QKiJVYj10E7msm8sYD4nINcB4YC7QT1XzCj2eCKwAjgJnqurW8EdpTMksmRjjERFpg0sUgrueZHMR290AvALMAH6pdtIaH7JkYowxpsJszMQYY0yFWTIxxhhTYZZMjDHGVJglE2OMMRVmycQYY0yFWTIxxhhTYZZMjDHGVJglE2OMMRVmycQYY0yF/X8UYmSPA/d+FAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlist=np.arange(0.6,5.4,0.1)\n",
    "ylist=[interpNDD(x,xilist,yilist) for x in xlist]\n",
    "plt.scatter(xilist, yilist, c = 'yellow', edgecolors = 'blue', s = 1200,marker = '*')\n",
    "plt.xlabel(\"X\", fontsize = 20)\n",
    "plt.ylabel(\"Y\", fontsize = 20)\n",
    "plt.plot(xlist, ylist, 'b-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e2df7750",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e160bb9dc0>]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xilist=[1,1.5,3,4.5,5]\n",
    "yilist=[0,0,2,0,0]\n",
    "xlist=np.arange(0.6,5.4,0.1)\n",
    "ylist=[interpNDD(x,xilist,yilist) for x in xlist]\n",
    "plt.scatter(xilist, yilist, c = 'yellow', edgecolors = 'blue', s = 1200,marker = '*')\n",
    "plt.xlabel(\"X\", fontsize = 20)\n",
    "plt.ylabel(\"Y\", fontsize = 20)\n",
    "plt.plot(xlist, ylist, 'b-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2d4513aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 点远离会减少对应误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5099c570",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
